{
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.4-final"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "Python 3.8.4 64-bit",
"display_name": "Python 3.8.4 64-bit",
"metadata": {
"interpreter": {
"hash": "f0532991493aaabfb050e25442c07985c94c4989d4e7fa5b7d1f9574004e3984"
}
}
}
},
"nbformat": 4,
"nbformat_minor": 2,
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Done with the parameters reset.\nPrevious Parameters are: {'hidden_layer_sizes': [10, 50, 100], 'activation': ['identity', 'relu', 'tanh', 'logistic'], 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'solver': ['lbfgs', 'sgd', 'adam']}\nCurrent Parameters are updated as: {'hidden_layer_sizes': [10], 'activation': ['relu'], 'learning_rate': ['constant'], 'solver': ['sgd']}\nDone with the parameters update.\nPrevious Parameters are: {'kernel': ['linear', 'poly', 'rbf', 'sigmoid'], 'C': [0.1, 1, 10]}\nCurrent Parameters are updated as: {'C': [0.1], 'kernel': ['linear']}\nDone with the parameters update.\nPrevious Parameters are: {'n_estimators': [50, 100, 150], 'learning_rate': [0.1, 1, 10, 100]}\nCurrent Parameters are updated as: {'n_estimators': [50], 'learning_rate': [1]}\nDone with the parameters update.\nPrevious Parameters are: {'n_estimators': [5, 50, 250], 'max_depth': [2, 4, 8, 16, 32]}\nCurrent Parameters are updated as: {'n_estimators': [50], 'max_depth': [2]}\nDone with the parameters update.\nPrevious Parameters are: {'n_estimators': [50, 100, 150, 200, 250, 300], 'max_depth': [1, 3, 5, 7, 9], 'learning_rate': [0.01, 0.1, 0.2, 0.3, 0.4]}\nCurrent Parameters are updated as: {'n_estimators': [50], 'max_depth': [2], 'learning_rate': [1]}\nDone with the parameters update.\nPrevious Parameters are: {'n_estimators': [50, 100, 150, 200, 250, 300], 'max_depth': [3, 5, 7, 9], 'learning_rate': [0.01, 0.1, 0.2, 0.3, 0.4], 'verbosity': [0]}\nCurrent Parameters are updated as: {'n_estimators': [50], 'max_depth': [2], 'learning_rate': [1]}\nDone with the parameters update.\n"
}
],
"source": [
"\n",
"from optimalflow.utilis_func import pipeline_splitting_rule, update_parameters,reset_parameters\n",
"reset_parameters()\n",
"\n",
"update_parameters(mode = \"cls\", estimator_name = \"mlp\", hidden_layer_sizes = [10],activation=[\"relu\"],learning_rate = [\"constant\"],solver = [\"sgd\"])\n",
"update_parameters(mode = \"cls\", estimator_name = \"svm\", C=[0.1],kernel=[\"linear\"])\n",
"update_parameters(mode = \"cls\", estimator_name = \"ada\", n_estimators =[50],learning_rate=[1])\n",
"update_parameters(mode = \"cls\", estimator_name = \"rf\", n_estimators =[50],max_depth=[2])\n",
"update_parameters(mode = \"cls\", estimator_name = \"gb\", n_estimators =[50],max_depth=[2],learning_rate=[1])\n",
"update_parameters(mode = \"cls\", estimator_name = \"xgb\", n_estimators =[50],max_depth=[2],learning_rate=[1])\n",
"\n",
"from optimalflow.autoPipe import autoPipe\n",
"import pandas as pd\n",
"from optimalflow.funcPP import PPtools\n",
"from optimalflow.autoPP import dynaPreprocessing\n",
"\n",
"from optimalflow.autoFS import dynaFS_clf\n",
"from optimalflow.autoCV import evaluate_model,dynaClassifier,dynaRegressor\n",
"df = pd.read_csv('./data/preprocessing/breast_cancer.csv')\n",
"custom_parameters = {\n",
" \"scaler\" : [\"None\", \"standard\"],\n",
" # threshold number of category dimension\n",
" \"encode_band\" : [4],\n",
" # low dimension encoding\n",
" \"low_encode\" : [\"onehot\",\"label\"], \n",
" # high dimension encoding\n",
" \"high_encode\" : [\"frequency\", \"mean\"],\n",
" \"winsorizer\" : [(0.05,0.05),(0.1,0.1)],\n",
" \"sparsity\" : [0.4],\n",
" \"cols\" : [1000]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"pipe = autoPipe(\n",
"[(\"autoPP\",dynaPreprocessing(custom_parameters = custom_parameters, label_col = 'diagnosis', model_type = \"cls\")),\n",
"(\"datasets_splitting\",pipeline_splitting_rule(val_size = 0.2, test_size = 0.2, random_state = 13)),\n",
"(\"autoFS\",dynaFS_clf(fs_num = 8, random_state=13, cv = 5, in_pipeline = True, input_from_file = False)),\n",
"(\"autoCV\",dynaClassifier(random_state = 13,cv_num = 5,in_pipeline = True, input_from_file = False)),\n",
"(\"model_evaluate\",evaluate_model(model_type = \"cls\"))])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Now in Progress - autoFS & autoCV Iteration: Estimate about 0.0 minutes left [####################] 100.0%\nThe top 5 Models with Best Performance Metrics:\n Dataset Model_Name \\\n3943 Dataset_563 mlp \n4312 Dataset_616 lgr \n3481 Dataset_497 mlp \n2546 Dataset_363 gb \n1139 Dataset_162 gb \n\n Best_Parameters \\\n3943 [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] \n4312 [('C', 100), ('random_state', 13)] \n3481 [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] \n2546 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n1139 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n\n Accuracy Precision Recall Latency \n3943 0.947 0.958 0.92 3.5 \n4312 0.947 0.923 0.96 3.0 \n3481 0.930 1.000 0.84 3.0 \n2546 0.930 0.957 0.88 1.0 \n1139 0.930 0.957 0.88 2.0 \n"
}
],
"source": [
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"pd.set_option('display.max_columns',None,'display.max_rows',None)\n",
"pd.set_option('max_colwidth', -1)\n",
"\n",
"DICT_PREPROCESSING,DICT_FEATURE_SELECTION,DICT_MODELS_EVALUATION,DICT_DATA,dyna_report= pipe.fit(df)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " Model_Name Accuracy Precision Recall Latency \\\n0 lgr 0.895 0.880 0.88 3.0 \n0 svm 0.912 0.885 0.92 3.0 \n0 mlp 0.439 0.439 1.00 3.0 \n0 rf 0.877 0.821 0.92 12.0 \n0 ada 0.912 0.955 0.84 17.0 \n0 gb 0.877 0.846 0.88 3.0 \n0 xgb 0.912 0.955 0.84 2.0 \n\n Best_Parameters \\\n0 [('C', 1000), ('random_state', 13)] \n0 [('C', 0.1), ('kernel', 'linear')] \n0 [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] \n0 [('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n0 [('learning_rate', 1), ('n_estimators', 50), ('random_state', 13)] \n0 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n0 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n\n Dataset \n0 Dataset_0 \n0 Dataset_0 \n0 Dataset_0 \n0 Dataset_0 \n0 Dataset_0 \n0 Dataset_0 \n0 Dataset_0 ",
"text/html": "
\n\n
\n \n \n | \n Model_Name | \n Accuracy | \n Precision | \n Recall | \n Latency | \n Best_Parameters | \n Dataset | \n
\n \n \n \n 0 | \n lgr | \n 0.895 | \n 0.880 | \n 0.88 | \n 3.0 | \n [('C', 1000), ('random_state', 13)] | \n Dataset_0 | \n
\n \n 0 | \n svm | \n 0.912 | \n 0.885 | \n 0.92 | \n 3.0 | \n [('C', 0.1), ('kernel', 'linear')] | \n Dataset_0 | \n
\n \n 0 | \n mlp | \n 0.439 | \n 0.439 | \n 1.00 | \n 3.0 | \n [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] | \n Dataset_0 | \n
\n \n 0 | \n rf | \n 0.877 | \n 0.821 | \n 0.92 | \n 12.0 | \n [('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n Dataset_0 | \n
\n \n 0 | \n ada | \n 0.912 | \n 0.955 | \n 0.84 | \n 17.0 | \n [('learning_rate', 1), ('n_estimators', 50), ('random_state', 13)] | \n Dataset_0 | \n
\n \n 0 | \n gb | \n 0.877 | \n 0.846 | \n 0.88 | \n 3.0 | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n Dataset_0 | \n
\n \n 0 | \n xgb | \n 0.912 | \n 0.955 | \n 0.84 | \n 2.0 | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n Dataset_0 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 18
}
],
"source": [
"\n",
"DICT_MODELS_EVALUATION['Dataset_0']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " Dataset Model_Name \\\n3943 Dataset_563 mlp \n4312 Dataset_616 lgr \n3481 Dataset_497 mlp \n2546 Dataset_363 gb \n1139 Dataset_162 gb \n3901 Dataset_557 mlp \n1314 Dataset_187 gb \n1412 Dataset_201 gb \n1678 Dataset_239 gb \n1916 Dataset_273 gb \n2644 Dataset_377 gb \n2840 Dataset_405 gb \n2851 Dataset_407 mlp \n2994 Dataset_427 gb \n3176 Dataset_453 gb \n\n Best_Parameters \\\n3943 [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] \n4312 [('C', 100), ('random_state', 13)] \n3481 [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] \n2546 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n1139 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n3901 [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] \n1314 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n1412 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n1678 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n1916 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n2644 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n2840 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n2851 [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] \n2994 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n3176 [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] \n\n Accuracy Precision Recall Latency \n3943 0.947 0.958 0.92 3.5 \n4312 0.947 0.923 0.96 3.0 \n3481 0.930 1.000 0.84 3.0 \n2546 0.930 0.957 0.88 1.0 \n1139 0.930 0.957 0.88 2.0 \n3901 0.930 0.957 0.88 2.0 \n1314 0.930 0.957 0.88 3.0 \n1412 0.930 0.957 0.88 3.0 \n1678 0.930 0.957 0.88 3.0 \n1916 0.930 0.957 0.88 3.0 \n2644 0.930 0.957 0.88 3.0 \n2840 0.930 0.957 0.88 3.0 \n2851 0.930 0.957 0.88 3.0 \n2994 0.930 0.957 0.88 3.0 \n3176 0.930 0.957 0.88 3.0 ",
"text/html": "\n\n
\n \n \n | \n Dataset | \n Model_Name | \n Best_Parameters | \n Accuracy | \n Precision | \n Recall | \n Latency | \n
\n \n \n \n 3943 | \n Dataset_563 | \n mlp | \n [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] | \n 0.947 | \n 0.958 | \n 0.92 | \n 3.5 | \n
\n \n 4312 | \n Dataset_616 | \n lgr | \n [('C', 100), ('random_state', 13)] | \n 0.947 | \n 0.923 | \n 0.96 | \n 3.0 | \n
\n \n 3481 | \n Dataset_497 | \n mlp | \n [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] | \n 0.930 | \n 1.000 | \n 0.84 | \n 3.0 | \n
\n \n 2546 | \n Dataset_363 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 1.0 | \n
\n \n 1139 | \n Dataset_162 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 2.0 | \n
\n \n 3901 | \n Dataset_557 | \n mlp | \n [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] | \n 0.930 | \n 0.957 | \n 0.88 | \n 2.0 | \n
\n \n 1314 | \n Dataset_187 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 3.0 | \n
\n \n 1412 | \n Dataset_201 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 3.0 | \n
\n \n 1678 | \n Dataset_239 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 3.0 | \n
\n \n 1916 | \n Dataset_273 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 3.0 | \n
\n \n 2644 | \n Dataset_377 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 3.0 | \n
\n \n 2840 | \n Dataset_405 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 3.0 | \n
\n \n 2851 | \n Dataset_407 | \n mlp | \n [('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')] | \n 0.930 | \n 0.957 | \n 0.88 | \n 3.0 | \n
\n \n 2994 | \n Dataset_427 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 3.0 | \n
\n \n 3176 | \n Dataset_453 | \n gb | \n [('learning_rate', 1), ('max_depth', 2), ('n_estimators', 50), ('random_state', 13)] | \n 0.930 | \n 0.957 | \n 0.88 | \n 3.0 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 19
}
],
"source": [
"dyna_report.head(15)\n",
"dyna_report.to_csv(\"dyna_report.csv\",index=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " concavity_mean concave points_mean perimeter_mean radius_mean \\\n264 0.09061 0.065270 111.60 17.19 \n231 0.01633 0.006588 71.76 11.32 \n197 0.11030 0.057780 117.40 18.08 \n172 0.20320 0.109700 102.50 15.46 \n54 0.05253 0.033340 97.26 15.10 \n33 0.16570 0.075930 127.90 19.27 \n68 0.25080 0.043750 60.73 9.72 \n237 0.09042 0.060220 132.50 20.48 \n51 0.01857 0.017230 87.21 13.64 \n196 0.13850 0.065260 90.63 13.77 \n\n texture_mean \n264 22.07 \n231 26.60 \n197 21.84 \n172 13.04 \n54 22.02 \n33 26.47 \n68 17.33 \n237 21.46 \n51 16.34 \n196 22.29 ",
"text/html": "\n\n
\n \n \n | \n concavity_mean | \n concave points_mean | \n perimeter_mean | \n radius_mean | \n texture_mean | \n
\n \n \n \n 264 | \n 0.09061 | \n 0.065270 | \n 111.60 | \n 17.19 | \n 22.07 | \n
\n \n 231 | \n 0.01633 | \n 0.006588 | \n 71.76 | \n 11.32 | \n 26.60 | \n
\n \n 197 | \n 0.11030 | \n 0.057780 | \n 117.40 | \n 18.08 | \n 21.84 | \n
\n \n 172 | \n 0.20320 | \n 0.109700 | \n 102.50 | \n 15.46 | \n 13.04 | \n
\n \n 54 | \n 0.05253 | \n 0.033340 | \n 97.26 | \n 15.10 | \n 22.02 | \n
\n \n 33 | \n 0.16570 | \n 0.075930 | \n 127.90 | \n 19.27 | \n 26.47 | \n
\n \n 68 | \n 0.25080 | \n 0.043750 | \n 60.73 | \n 9.72 | \n 17.33 | \n
\n \n 237 | \n 0.09042 | \n 0.060220 | \n 132.50 | \n 20.48 | \n 21.46 | \n
\n \n 51 | \n 0.01857 | \n 0.017230 | \n 87.21 | \n 13.64 | \n 16.34 | \n
\n \n 196 | \n 0.13850 | \n 0.065260 | \n 90.63 | \n 13.77 | \n 22.29 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 15
}
],
"source": [
"DICT_DATA['Dataset_0']['DICT_TEST'][\"X\"].head(10)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "\"winsor_0-Scaler_None-- Encoded Features:['diagnosis', 'Size_3', 'area_mean', 'compactness_mean', 'concave points_mean', 'concavity_mean', 'fractal_dimension_mean', 'perimeter_mean', 'radius_mean', 'smoothness_mean', 'symmetry_mean', 'texture_mean', 'Frequency_Age', 'onehot_Position_1_left', 'onehot_Position_1_right', 'Frequency_Position_2', 'Frequency_Size_1', 'Frequency_Size_2', 'onehot_Treatment_no-recurrence-events', 'onehot_Treatment_recurrence-events', 'onehot_Type_1_ge40', 'onehot_Type_1_lt40', 'onehot_Type_1_premeno', 'onehot_Type_2_NaN', 'onehot_Type_2_no', 'onehot_Type_2_yes', 'onehot_Type_3_no', 'onehot_Type_3_yes']\""
},
"metadata": {},
"execution_count": 21
}
],
"source": [
"test = DICT_PREPROCESSING['Dataset_0']\n",
"test"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"def save_obj(obj, name ):\n",
" with open(name + '.pkl', 'wb') as f:\n",
" pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
"\n",
"def load_obj(name ):\n",
" with open(name + '.pkl', 'rb') as f:\n",
" return pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"save_obj(DICT_PREPROCESSING,\"dict_preprocess\")\n",
"save_obj(DICT_DATA,\"dict_data\")\n",
"save_obj(DICT_MODELS_EVALUATION,\"dict_models_evaluate\")\n",
"save_obj(dyna_report,\"dyna_report\")"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "\"winsor_0-Scaler_None-- Encoded Features:['diagnosis', 'Size_3', 'area_mean', 'compactness_mean', 'concave points_mean', 'concavity_mean', 'fractal_dimension_mean', 'perimeter_mean', 'radius_mean', 'smoothness_mean', 'symmetry_mean', 'texture_mean', 'Frequency_Age', 'onehot_Position_1_left', 'onehot_Position_1_right', 'Frequency_Position_2', 'Frequency_Size_1', 'Frequency_Size_2', 'onehot_Treatment_no-recurrence-events', 'onehot_Treatment_recurrence-events', 'onehot_Type_1_ge40', 'onehot_Type_1_lt40', 'onehot_Type_1_premeno', 'onehot_Type_2_NaN', 'onehot_Type_2_no', 'onehot_Type_2_yes', 'onehot_Type_3_no', 'onehot_Type_3_yes']\""
},
"metadata": {},
"execution_count": 2
}
],
"source": [
"DICT_PREP = load_obj(\"dict_preprocess\")\n",
"dyna_report = load_obj(\"dyna_report\")\n",
"DICT_PREP['Dataset_0']"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from optimalflow.autoViz import autoViz\n",
"viz = autoViz(preprocess_dict=DICT_PREP,report=dyna_report)\n",
"viz.clf_model_retrieval(metrics='accuracy')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " Dataset Encode_low_dimension Encode_high_dimension Winsorize \\\n0 Dataset_0 Low Dim_Onehot High Dim_Frequency Winsorize_0 \n1 Dataset_0 Low Dim_Onehot High Dim_Frequency Winsorize_0 \n2 Dataset_0 Low Dim_Onehot High Dim_Frequency Winsorize_0 \n\n Scale Accuracy Level cnt \n0 Scale_No Scaler 0.912 Top Accuracy 1 \n1 Scale_No Scaler 0.912 Top Accuracy 1 \n2 Scale_No Scaler 0.912 Top Accuracy 1 ",
"text/html": "\n\n
\n \n \n | \n Dataset | \n Encode_low_dimension | \n Encode_high_dimension | \n Winsorize | \n Scale | \n Accuracy | \n Level | \n cnt | \n
\n \n \n \n 0 | \n Dataset_0 | \n Low Dim_Onehot | \n High Dim_Frequency | \n Winsorize_0 | \n Scale_No Scaler | \n 0.912 | \n Top Accuracy | \n 1 | \n
\n \n 1 | \n Dataset_0 | \n Low Dim_Onehot | \n High Dim_Frequency | \n Winsorize_0 | \n Scale_No Scaler | \n 0.912 | \n Top Accuracy | \n 1 | \n
\n \n 2 | \n Dataset_0 | \n Low Dim_Onehot | \n High Dim_Frequency | \n Winsorize_0 | \n Scale_No Scaler | \n 0.912 | \n Top Accuracy | \n 1 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 23
},
{
"output_type": "execute_result",
"data": {
"text/plain": " Dataset Encode_low_dimension Encode_high_dimension Winsorize \\\n0 Dataset_0 Low Dim_Onehot High Dim_Frequency Winsorize_0 \n1 Dataset_0 Low Dim_Onehot High Dim_Frequency Winsorize_0 \n2 Dataset_0 Low Dim_Onehot High Dim_Frequency Winsorize_0 \n\n Scale Precision Level cnt \n0 Scale_Scale_No Scaler 0.955 Top Precision 1 \n1 Scale_Scale_No Scaler 0.955 Top Precision 1 \n2 Scale_Scale_No Scaler 0.885 High Precision 1 ",
"text/html": "\n\n
\n \n \n | \n Dataset | \n Encode_low_dimension | \n Encode_high_dimension | \n Winsorize | \n Scale | \n Precision | \n Level | \n cnt | \n
\n \n \n \n 0 | \n Dataset_0 | \n Low Dim_Onehot | \n High Dim_Frequency | \n Winsorize_0 | \n Scale_Scale_No Scaler | \n 0.955 | \n Top Precision | \n 1 | \n
\n \n 1 | \n Dataset_0 | \n Low Dim_Onehot | \n High Dim_Frequency | \n Winsorize_0 | \n Scale_Scale_No Scaler | \n 0.955 | \n Top Precision | \n 1 | \n
\n \n 2 | \n Dataset_0 | \n Low Dim_Onehot | \n High Dim_Frequency | \n Winsorize_0 | \n Scale_Scale_No Scaler | \n 0.885 | \n High Precision | \n 1 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 23
},
{
"output_type": "execute_result",
"data": {
"text/plain": " Dataset Encode_low_dimension Encode_high_dimension Winsorize \\\n0 Dataset_0 Low Dim_Onehot High Dim_Frequency Winsorize_0 \n1 Dataset_0 Low Dim_Onehot High Dim_Frequency Winsorize_0 \n2 Dataset_0 Low Dim_Onehot High Dim_Frequency Winsorize_0 \n\n Scale Recall Level cnt \n0 Scale_Scale_No Scaler 0.84 High Recall 1 \n1 Scale_Scale_No Scaler 0.84 High Recall 1 \n2 Scale_Scale_No Scaler 0.92 Top Recall 1 ",
"text/html": "\n\n
\n \n \n | \n Dataset | \n Encode_low_dimension | \n Encode_high_dimension | \n Winsorize | \n Scale | \n Recall | \n Level | \n cnt | \n
\n \n \n \n 0 | \n Dataset_0 | \n Low Dim_Onehot | \n High Dim_Frequency | \n Winsorize_0 | \n Scale_Scale_No Scaler | \n 0.84 | \n High Recall | \n 1 | \n
\n \n 1 | \n Dataset_0 | \n Low Dim_Onehot | \n High Dim_Frequency | \n Winsorize_0 | \n Scale_Scale_No Scaler | \n 0.84 | \n High Recall | \n 1 | \n
\n \n 2 | \n Dataset_0 | \n Low Dim_Onehot | \n High Dim_Frequency | \n Winsorize_0 | \n Scale_Scale_No Scaler | \n 0.92 | \n Top Recall | \n 1 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 23
}
],
"source": [
"import re\n",
"import pandas as pd\n",
"\n",
"columns = [\"Dataset\",\"Encode_low_dimension\",\"Encode_high_dimension\",\"Winsorize\",\"Scale\"]\n",
"df_pp = pd.DataFrame(columns=columns)\n",
"\n",
"for i in list(DICT_PREPROCESSING.keys()):\n",
" row_pp = [i]\n",
" s = DICT_PREPROCESSING[i]\n",
" ext = re.search(\"Encoded Features:(.*)']\", s).group(1)\n",
" \n",
" if (\"onehot_\" in ext) and (\"Frequency_\" in ext):\n",
" row_pp.append('Low Dim_Onehot')\n",
" row_pp.append('High Dim_Frequency')\n",
" row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
" row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
" df_pp.loc[len(df_pp)] = row_pp\n",
" elif (\"onehot_\" in ext) and (\"Mean_\" in ext):\n",
" row_pp.append('Low Dim_Onehot')\n",
" row_pp.append('High Dim_Mean')\n",
" row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
" row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
" df_pp.loc[len(df_pp)] = row_pp\n",
" elif (\"onehot_\" in ext) and (\"Mean_\" not in ext) and (\"Frequency_\" not in ext):\n",
" row_pp.append('Low Dim_Onehot')\n",
" row_pp.append('High Dim_No Encoder')\n",
" row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
" row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
" df_pp.loc[len(df_pp)] = row_pp\n",
" elif (\"Label_\" in ext) and (\"Frequency_\" in ext):\n",
" row_pp.append('Low Dim_Label')\n",
" row_pp.append('High Dim_Frequency')\n",
" row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
" row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
" df_pp.loc[len(df_pp)] = row_pp\n",
" elif (\"Label_\" in ext) and (\"Mean_\" in ext):\n",
" row_pp.append('Low Dim_Label')\n",
" row_pp.append('High Dim_Mean')\n",
" row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
" row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
" df_pp.loc[len(df_pp)] = row_pp\n",
" elif (\"Label_\" in ext) and (\"Mean_\" not in ext) and (\"Frequency_\" not in ext):\n",
" row_pp.append('Low Dim_Label')\n",
" row_pp.append('High Dim_No Encoder')\n",
" row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
" row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
" df_pp.loc[len(df_pp)] = row_pp\n",
" elif (\"Frequency_\" in ext) and (\"onehot_\" not in ext) and (\"Label_\" not in ext):\n",
" row_pp.append('Low Dim_No Encoder')\n",
" row_pp.append('High Dim_Frequency')\n",
" row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
" row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
" df_pp.loc[len(df_pp)] = row_pp \n",
" elif (\"Mean_\" in ext) and (\"onehot_\" not in ext) and (\"Label_\" not in ext):\n",
" row_pp.append('Low Dim_No Encoder')\n",
" row_pp.append('High Dim_Mean')\n",
" row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
" row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
" df_pp.loc[len(df_pp)] = row_pp \n",
" elif (\"Frequency_\" not in ext) and (\"Mean_\" not in ext) and (\"onehot_\" not in ext) and (\"Label_\" not in ext):\n",
" row_pp.append('Low Dim_No Encoder')\n",
" row_pp.append('High Dim_No Encoder')\n",
" row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))\n",
" row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))\n",
" df_pp.loc[len(df_pp)] = row_pp \n",
"\n",
"\n",
"df_report_Accuracy = df_pp.merge(dyna_report[['Dataset','Accuracy']], how = 'left', on = 'Dataset')\n",
"bins = [0, 0.70, 0.90, 1]\n",
"labels = [\"Low Accuracy\",\"High Accuracy\",\"Top Accuracy\"]\n",
"df_report_Accuracy['Level'] = pd.cut(df_report_Accuracy['Accuracy'], bins=bins, labels=labels)\n",
"df_report_Accuracy['cnt'] = 1\n",
"df_report_Accuracy.loc[df_report_Accuracy['Scale'] == 'None','Scale'] = \"No Scaler\"\n",
"df_report_Accuracy['Scale'] = 'Scale_'+df_report_Accuracy['Scale']\n",
"df_report_Accuracy['Winsorize'] = 'Winsorize_' + df_report_Accuracy['Winsorize']\n",
"df_report_Accuracy.head(3)\n",
"\n",
"df_report_Precision = df_pp.merge(dyna_report[['Dataset','Precision']], how = 'left', on = 'Dataset')\n",
"bins = [0, 0.70, 0.90, 1]\n",
"labels = [\"Low Precision\",\"High Precision\",\"Top Precision\"]\n",
"df_report_Precision['Level'] = pd.cut(df_report_Precision['Precision'], bins=bins, labels=labels)\n",
"df_report_Precision['cnt'] = 1\n",
"df_report_Precision.loc[df_report_Precision['Scale'] == 'None','Scale'] = \"No Scaler\"\n",
"df_report_Precision['Scale'] = 'Scale_'+df_report_Accuracy['Scale']\n",
"df_report_Precision['Winsorize'] = 'Winsorize_' + df_report_Precision['Winsorize']\n",
"df_report_Precision.head(3)\n",
"\n",
"df_report_Recall = df_pp.merge(dyna_report[['Dataset','Recall']], how = 'left', on = 'Dataset')\n",
"bins = [0, 0.70, 0.90, 1]\n",
"labels = [\"Low Recall\",\"High Recall\",\"Top Recall\"]\n",
"df_report_Recall['Level'] = pd.cut(df_report_Recall['Recall'], bins=bins, labels=labels)\n",
"df_report_Recall['cnt'] = 1\n",
"df_report_Recall.loc[df_report_Recall['Scale'] == 'None','Scale'] = \"No Scaler\"\n",
"df_report_Recall['Scale'] = 'Scale_'+df_report_Accuracy['Scale']\n",
"df_report_Recall['Winsorize'] = 'Winsorize_' + df_report_Recall['Winsorize']\n",
"df_report_Recall.head(3)\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " antecedentIndex consequentIndex Total\n0 Dataset_0 Low Dim_Onehot 7.0 \n1 Dataset_1 Low Dim_Onehot 7.0 \n2 Dataset_10 Low Dim_Onehot 7.0 \n3 Dataset_100 Low Dim_Onehot 7.0 \n4 Dataset_101 Low Dim_Onehot 7.0 \n5 Dataset_102 Low Dim_Onehot 7.0 \n6 Dataset_103 Low Dim_Onehot 7.0 \n7 Dataset_104 Low Dim_Onehot 7.0 \n8 Dataset_105 Low Dim_Onehot 7.0 \n9 Dataset_106 Low Dim_Onehot 7.0 ",
"text/html": "\n\n
\n \n \n | \n antecedentIndex | \n consequentIndex | \n Total | \n
\n \n \n \n 0 | \n Dataset_0 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n 1 | \n Dataset_1 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n 2 | \n Dataset_10 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n 3 | \n Dataset_100 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n 4 | \n Dataset_101 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n 5 | \n Dataset_102 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n 6 | \n Dataset_103 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n 7 | \n Dataset_104 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n 8 | \n Dataset_105 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n 9 | \n Dataset_106 | \n Low Dim_Onehot | \n 7.0 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 24
}
],
"source": [
"step1_df = df_report_Accuracy.groupby(['Encode_low_dimension','Dataset'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Dataset\":\"antecedentIndex\",\"Encode_low_dimension\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']]\n",
"step2_df = df_report_Accuracy.groupby(['Encode_low_dimension','Encode_high_dimension'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Encode_low_dimension\":\"antecedentIndex\",\"Encode_high_dimension\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']]\n",
"step3_df = df_report_Accuracy.groupby(['Encode_high_dimension','Winsorize'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Encode_high_dimension\":\"antecedentIndex\",\"Winsorize\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']]\n",
"step4_df = df_report_Accuracy.groupby(['Winsorize','Scale'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Winsorize\":\"antecedentIndex\",\"Scale\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']]\n",
"step5_df = df_report_Accuracy.groupby(['Scale','Level'], as_index=False)['cnt'].count().rename({\"cnt\":\"Total\",\"Scale\":\"antecedentIndex\",\"Level\":\"consequentIndex\"},axis = 1)[['antecedentIndex','consequentIndex','Total']].dropna()\n",
"integrated_df = pd.concat([step1_df,step2_df,step3_df,step4_df,step5_df],axis = 0)\n",
"integrated_df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"label_df = pd.DataFrame(integrated_df['antecedentIndex'].append(integrated_df['consequentIndex']).drop_duplicates(),columns = {\"label\"})\n",
"label_df['Number'] = label_df.reset_index().index\n",
"label_list = list(label_df.label)\n",
"\n",
"\n",
"\n",
"source_df = pd.DataFrame(integrated_df['antecedentIndex'])\n",
"source_df = source_df.merge(label_df, left_on=['antecedentIndex'], right_on = ['label'],how = 'left')\n",
"source_list = list(source_df['Number'])\n",
"\n",
"\n",
"target_df = pd.DataFrame(integrated_df['consequentIndex'])\n",
"target_df = target_df.merge(label_df, left_on=['consequentIndex'], right_on = ['label'],how = 'left')\n",
"target_list = list(target_df['Number'])\n",
"\n",
"value_list = [int(i) for i in list(integrated_df.Total)]\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
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"x": 0.05
},
"xaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
},
"yaxis": {
"automargin": true,
"gridcolor": "white",
"linecolor": "white",
"ticks": "",
"title": {
"standoff": 15
},
"zerolinecolor": "white",
"zerolinewidth": 2
}
}
},
"title": {
"text": "Pipeline Cluster Traversal Experiments - autoViz Model Retrieval Diagram ©Tony Dong"
}
}
}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": "'temp-plot.html'"
},
"metadata": {},
"execution_count": 26
}
],
"source": [
"import plotly.graph_objects as go\n",
"\n",
"fig = go.Figure(data=[go.Sankey(\n",
" node = dict(\n",
" pad = 15,\n",
" thickness = 10,\n",
" line = dict(color = 'rgb(25,100,90)', width = 0.5),\n",
" label = label_list,\n",
" color = 'rgb(71,172,55)'\n",
" ),\n",
" link = dict(\n",
" source = source_list, \n",
" target = target_list,\n",
" value = value_list\n",
" ))])\n",
"\n",
"fig.update_layout(title = 'Pipeline Cluster Traversal Experiments - autoViz Model Retrieval Diagram ©Tony Dong', font_size=8)\n",
"from plotly.offline import plot\n",
"plot(fig)\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": "'pip' is not recognized as an internal or external command,\noperable program or batch file.\n"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "[('learning_rate', 1),\n ('max_depth', 2),\n ('n_estimators', 50),\n ('random_state', 13)]"
},
"metadata": {},
"execution_count": 13
}
],
"source": [
"a = {'learning_rate': 1, 'max_depth': 2, 'n_estimators': 50, 'random_state': 13}\n",
"lis = a.items()\n",
"[i for i in lis]"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from optimalflow.autoViz import autoViz\n",
"import pandas as pd\n",
"\n",
"df = pd.read_csv('dyna_report.csv')\n",
"a = autoViz(preprocess_dict = None,report = df)\n",
"a.table_report()\n"
]
},
{
"source": [
"Comparing classic ml workflow"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.svm import SVC\n",
"from sklearn.datasets import load_digits\n",
"from sklearn.model_selection import learning_curve\n",
"from sklearn.model_selection import ShuffleSplit\n",
"\n",
"\n",
"def plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None,\n",
" n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
" \"\"\"\n",
" \"\"\"\n",
" if axes is None:\n",
" _, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
"\n",
" axes[0].set_title(title)\n",
" if ylim is not None:\n",
" axes[0].set_ylim(*ylim)\n",
" axes[0].set_xlabel(\"Training examples\")\n",
" axes[0].set_ylabel(\"Score\")\n",
"\n",
" train_sizes, train_scores, test_scores, fit_times, _ = \\\n",
" learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,\n",
" train_sizes=train_sizes,\n",
" return_times=True)\n",
" train_scores_mean = np.mean(train_scores, axis=1)\n",
" train_scores_std = np.std(train_scores, axis=1)\n",
" test_scores_mean = np.mean(test_scores, axis=1)\n",
" test_scores_std = np.std(test_scores, axis=1)\n",
" fit_times_mean = np.mean(fit_times, axis=1)\n",
" fit_times_std = np.std(fit_times, axis=1)\n",
"\n",
" # Plot learning curve\n",
" axes[0].grid()\n",
" axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
" train_scores_mean + train_scores_std, alpha=0.1,\n",
" color=\"r\")\n",
" axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
" test_scores_mean + test_scores_std, alpha=0.1,\n",
" color=\"g\")\n",
" axes[0].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
" label=\"Training score\")\n",
" axes[0].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
" label=\"Cross-validation score\")\n",
" axes[0].legend(loc=\"best\")\n",
"\n",
" # Plot n_samples vs fit_times\n",
" axes[1].grid()\n",
" axes[1].plot(train_sizes, fit_times_mean, 'o-')\n",
" axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,\n",
" fit_times_mean + fit_times_std, alpha=0.1)\n",
" axes[1].set_xlabel(\"Training examples\")\n",
" axes[1].set_ylabel(\"fit_times\")\n",
" axes[1].set_title(\"Scalability of the model\")\n",
"\n",
" # Plot fit_time vs score\n",
" axes[2].grid()\n",
" axes[2].plot(fit_times_mean, test_scores_mean, 'o-')\n",
" axes[2].fill_between(fit_times_mean, test_scores_mean - test_scores_std,\n",
" test_scores_mean + test_scores_std, alpha=0.1)\n",
" axes[2].set_xlabel(\"fit_times\")\n",
" axes[2].set_ylabel(\"Score\")\n",
" axes[2].set_title(\"Performance of the model\")\n",
"\n",
" return plt\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, axes = plt.subplots(3, 1, figsize=(10, 15))\n",
"\n",
"X, y = load_digits(return_X_y=True)\n",
"\n",
"title = \"Learning Curves\"\n",
"# Cross validation with 100 iterations to get smoother mean test and train\n",
"# score curves, each time with 20% data randomly selected as a validation set.\n",
"cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
"\n",
"estimator = GaussianNB()\n",
"plot_learning_curve(estimator, title, X, y, axes=axes[:, 0], ylim=(0.7, 1.01),\n",
" cv=cv, n_jobs=4)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": []
},
{
"source": [
"Dataset\tModel_Name\tBest_Parameters\tAccuracy\tPrecision\tRecall\tLatency\n",
"3943\tDataset_563\tmlp\t[('activation', 'relu'), ('hidden_layer_sizes', (10,)), ('learning_rate', 'constant'), ('random_state', 13), ('solver', 'sgd')]\t0.947\t0.958\t0.92\t3.5"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"dict_keys(['X', 'y'])"
]
},
"metadata": {},
"execution_count": 10
}
],
"source": [
"dict_data = load_obj(\"dict_data\")\n",
"dict_data[\"Dataset_563\"]['DICT_Train'].keys()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"dict_data = load_obj(\"dict_data\")\n",
"X = dict_data[\"Dataset_563\"]['DICT_Train']['X']\n",
"y = dict_data[\"Dataset_563\"]['DICT_Train']['y']"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"metadata": {},
"execution_count": 55
}
],
"source": [
"X.shape\n",
"type(X)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(171,)"
]
},
"metadata": {},
"execution_count": 46
}
],
"source": [
"y.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(171, 1)"
]
},
"metadata": {},
"execution_count": 53
}
],
"source": [
"import pandas as pd\n",
"test = pd.DataFrame(y)\n",
"test.head()\n",
"test = test.to_numpy()\n",
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"X_digits, y_digits = load_digits(return_X_y=True)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"metadata": {},
"execution_count": 57
}
],
"source": [
"type(X_digits)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[-0.47171866, -0.25147904, -0.67443392, ..., -0.20077146,\n",
" 0.14041893, -0.16963779],\n",
" [ 0.45796268, 0.22989631, 1.66854302, ..., 0.94593156,\n",
" -1.15682917, -0.16963779],\n",
" [-0.7996747 , -0.87043406, -0.41695619, ..., -0.28920614,\n",
" 0.36982133, -0.16963779],\n",
" ...,\n",
" [-0.01869197, -0.34514378, 0.03820252, ..., 0.25024541,\n",
" 0.05225644, -0.16963779],\n",
" [-0.95971473, -0.96287041, -0.71312592, ..., -0.81391858,\n",
" -0.57297756, -0.16963779],\n",
" [-0.5047978 , 0.5262454 , -1.19396205, ..., -0.35995389,\n",
" -0.09887926, -0.16963779]])"
]
},
"metadata": {},
"execution_count": 59
}
],
"source": [
"X.to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1,\n",
" 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0,\n",
" 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0,\n",
" 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1,\n",
" 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,\n",
" 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
" 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0])"
]
},
"metadata": {},
"execution_count": 61
}
],
"source": [
"y.to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0, 1, 2, ..., 8, 9, 8])"
]
},
"metadata": {},
"execution_count": 60
}
],
"source": [
"y_digits"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 0., 0., 5., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 10., 0., 0.],\n",
" [ 0., 0., 0., ..., 16., 9., 0.],\n",
" ...,\n",
" [ 0., 0., 1., ..., 6., 0., 0.],\n",
" [ 0., 0., 2., ..., 12., 0., 0.],\n",
" [ 0., 0., 10., ..., 12., 1., 0.]])"
]
},
"metadata": {},
"execution_count": 58
}
],
"source": [
"X_digits"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"output_type": "error",
"ename": "IndexError",
"evalue": "too many indices for array: array is 1-dimensional, but 2 were indexed",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m# estimator = MLPClassifier(random_state=13, activation = 'relu',hidden_layer_sizes = (10,),learning_rate = 'constant',solver='sgd')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[0mestimator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGaussianNB\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[0mplot_learning_curve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_pd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pd\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mylim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.01\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: too many indices for array: array is 1-dimensional, but 2 were indexed"
]
}
],
"source": [
"from sklearn.neural_network import MLPClassifier\n",
"\n",
"import pickle\n",
"def save_obj(obj, name ):\n",
" with open(name + '.pkl', 'wb') as f:\n",
" pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n",
"\n",
"def load_obj(name ):\n",
" with open(name + '.pkl', 'rb') as f:\n",
" return pickle.load(f)\n",
"# X_pd = X.to_numpy()\n",
"# y_pd = y.to_numpy()\n",
"X_pd = X_digits\n",
"y_pd = y_digits\n",
"\n",
"fig, axes = plt.subplots(3, 1, figsize=(10, 15))\n",
"title = \"Learning Curves\"\n",
"# Cross validation with 100 iterations to get smoother mean test and train\n",
"# score curves, each time with 20% data randomly selected as a validation set.\n",
"cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
"\n",
"# estimator = MLPClassifier(random_state=13, activation = 'relu',hidden_layer_sizes = (10,),learning_rate = 'constant',solver='sgd')\n",
"estimator = GaussianNB()\n",
"plot_learning_curve(estimator, title, X_pd, y_pd, axes=axes[:, 0], ylim=(0.7, 1.01),cv=cv, n_jobs=4)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "",
"image/svg+xml": "\r\n\r\n\r\n\r\n",
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.svm import SVC\n",
"from sklearn.datasets import load_digits\n",
"from sklearn.model_selection import learning_curve\n",
"from sklearn.model_selection import ShuffleSplit\n",
"\n",
"\n",
"def plot_learning_curve(estimator, title, X, y, axes=None, ylim=None, cv=None,\n",
" n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
" if axes is None:\n",
" _, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
"\n",
" axes[0].set_title(title)\n",
" if ylim is not None:\n",
" axes[0].set_ylim(*ylim)\n",
" axes[0].set_xlabel(\"Training examples\")\n",
" axes[0].set_ylabel(\"Score\")\n",
"\n",
" train_sizes, train_scores, test_scores, fit_times, _ = \\\n",
" learning_curve(estimator, X, y, cv=cv, n_jobs=n_jobs,\n",
" train_sizes=train_sizes,\n",
" return_times=True)\n",
" train_scores_mean = np.mean(train_scores, axis=1)\n",
" train_scores_std = np.std(train_scores, axis=1)\n",
" test_scores_mean = np.mean(test_scores, axis=1)\n",
" test_scores_std = np.std(test_scores, axis=1)\n",
" fit_times_mean = np.mean(fit_times, axis=1)\n",
" fit_times_std = np.std(fit_times, axis=1)\n",
"\n",
" # Plot learning curve\n",
" axes[0].grid()\n",
" axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
" train_scores_mean + train_scores_std, alpha=0.1,\n",
" color=\"r\")\n",
" axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
" test_scores_mean + test_scores_std, alpha=0.1,\n",
" color=\"g\")\n",
" axes[0].plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
" label=\"Training score\")\n",
" axes[0].plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
" label=\"Cross-validation score\")\n",
" axes[0].legend(loc=\"best\")\n",
"\n",
" # Plot n_samples vs fit_times\n",
" axes[1].grid()\n",
" axes[1].plot(train_sizes, fit_times_mean, 'o-')\n",
" axes[1].fill_between(train_sizes, fit_times_mean - fit_times_std,\n",
" fit_times_mean + fit_times_std, alpha=0.1)\n",
" axes[1].set_xlabel(\"Training examples\")\n",
" axes[1].set_ylabel(\"fit_times\")\n",
" axes[1].set_title(\"Scalability of the model\")\n",
"\n",
" # Plot fit_time vs score\n",
" axes[2].grid()\n",
" axes[2].plot(fit_times_mean, test_scores_mean, 'o-')\n",
" axes[2].fill_between(fit_times_mean, test_scores_mean - test_scores_std,\n",
" test_scores_mean + test_scores_std, alpha=0.1)\n",
" axes[2].set_xlabel(\"fit_times\")\n",
" axes[2].set_ylabel(\"Score\")\n",
" axes[2].set_title(\"Performance of the model\")\n",
"\n",
" return plt\n",
"\n",
"\n",
"fig, axes = plt.subplots(3, 2, figsize=(10, 15))\n",
"\n",
"# X, y = load_digits(return_X_y=True)\n",
"\n",
"dict_data = load_obj(\"dict_data\")\n",
"X = dict_data[\"Dataset_563\"]['DICT_Train']['X']\n",
"y = dict_data[\"Dataset_563\"]['DICT_Train']['y']\n",
"\n",
"X_pd = X.to_numpy()\n",
"y_pd = y.to_numpy()\n",
"\n",
"title = \"Learning Curves (OptimalFlow)\"\n",
"# Cross validation with 100 iterations to get smoother mean test and train\n",
"# score curves, each time with 20% data randomly selected as a validation set.\n",
"cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)\n",
"\n",
"estimator = MLPClassifier(random_state=13, activation = 'relu',hidden_layer_sizes = (10,),learning_rate = 'constant',solver='sgd')\n",
"plot_learning_curve(estimator, title, X_pd, y_pd, axes=axes[:, 0], ylim=(0.7, 1.01),\n",
" cv=cv, n_jobs=4)\n",
"\n",
"title = r\"Learning Curves (SVM, RBF kernel, $\\gamma=0.001$)\"\n",
"# SVC is more expensive so we do a lower number of CV iterations:\n",
"cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)\n",
"estimator = SVC(gamma=0.001)\n",
"plot_learning_curve(estimator, title, X_pd, y_pd, axes=axes[:, 1], ylim=(0.2, 1.01),\n",
" cv=cv, n_jobs=4)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"204 0\n",
"198 1\n",
"93 0\n",
"78 1\n",
"128 0\n",
"Name: diagnosis, dtype: int32"
]
},
"metadata": {},
"execution_count": 34
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "",
"image/svg+xml": "\r\n\r\n\r\n\r\n",
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {},
"execution_count": 36
}
],
"source": [
"dict_data = load_obj(\"dict_data\")\n",
"X = dict_data[\"Dataset_563\"]['DICT_Train']['X']\n",
"y = dict_data[\"Dataset_563\"]['DICT_Train']['y']\n",
"# Instantiate the visualizer\n",
"visualizer = ParallelCoordinates(shuffle=True)\n",
"# Fit the visualizer and display it\n",
"visualizer.fit_transform(X, y)\n",
"visualizer.show()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" temperature relative humidity light CO2 humidity\n",
"0 23.18 27.2720 426.0 721.25 0.004793\n",
"1 23.15 27.2675 429.5 714.00 0.004783\n",
"2 23.15 27.2450 426.0 713.50 0.004779\n",
"3 23.15 27.2000 426.0 708.25 0.004772\n",
"4 23.10 27.2000 426.0 704.50 0.004757"
],
"text/html": "\n\n
\n \n \n | \n temperature | \n relative humidity | \n light | \n CO2 | \n humidity | \n
\n \n \n \n 0 | \n 23.18 | \n 27.2720 | \n 426.0 | \n 721.25 | \n 0.004793 | \n
\n \n 1 | \n 23.15 | \n 27.2675 | \n 429.5 | \n 714.00 | \n 0.004783 | \n
\n \n 2 | \n 23.15 | \n 27.2450 | \n 426.0 | \n 713.50 | \n 0.004779 | \n
\n \n 3 | \n 23.15 | \n 27.2000 | \n 426.0 | \n 708.25 | \n 0.004772 | \n
\n \n 4 | \n 23.10 | \n 27.2000 | \n 426.0 | \n 704.50 | \n 0.004757 | \n
\n \n
\n
"
},
"metadata": {},
"execution_count": 33
}
],
"source": [
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "